R Notebook

library(ggplot2)
library(grid)
library(forestploter)
library(tidyverse)

saveto <- "/Volumes/Research Project/IAT_Retest/Plotting/SavedFigs/"

path <- "/Volumes/"
setwd(paste0(path,"Research Project/IAT_Retest/Estimates/UCR/"))

Warning: The working directory was changed to /Volumes/Research Project/IAT_Retest/Estimates/UCR inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.

Cal1 <-read.csv("IndividualParametersQuadUCR1.csv", header = T)
Cal2 <-read.csv("IndividualParametersQuadUCR2.csv", header = T)

colnames(Cal1)[2:ncol(Cal1)]<-paste(colnames(Cal1[2:ncol(Cal1)]),"T1",sep="_")
colnames(Cal2)[2:ncol(Cal2)]<-paste(colnames(Cal2[2:ncol(Cal2)]),"T2",sep="_")

Q.Cal <- merge(Cal1, Cal2, by = "subject", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/PI/"))
PI1 <- read.csv("IndividualParametersQuadProjectImplicit1.csv")
PI2 <- read.csv("IndividualParametersQuadProjectImplicit2.csv")

colnames(PI1)[2:ncol(PI1)]<-paste(colnames(PI1[2:ncol(PI1)]),"T1",sep="_")
colnames(PI2)[2:ncol(PI2)]<-paste(colnames(PI2[2:ncol(PI2)]),"T2",sep="_")

Q.PI <- merge(PI1, PI2, by = "ID", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Gawronski/"))
Gaw1 <- read.csv("IndividualParametersQuadGawronski1.csv")
Gaw2 <- read.csv("IndividualParametersQuadGawronski2.csv")

colnames(Gaw1)[2:ncol(Gaw1)]<-paste(colnames(Gaw1[2:ncol(Gaw1)]),"T1",sep="_")
colnames(Gaw2)[2:ncol(Gaw2)]<-paste(colnames(Gaw2[2:ncol(Gaw2)]),"T2",sep="_")

Q.Gaw <- merge(Gaw1, Gaw2, by = "code", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Forscher/"))
For1<-read.csv("IndividualParametersQuadForscher1.csv", header = T)
For2<-read.csv("IndividualParametersQuadForscher2.csv", header = T)

colnames(For1)[2:ncol(For1)]<-paste(colnames(For1[2:ncol(For1)]),"T1",sep="_")
colnames(For2)[2:ncol(For2)]<-paste(colnames(For2[2:ncol(For2)]),"T2",sep="_")

Q.For <- merge(For1, For2, by = "subject", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Lai1/"))
Lai11<-read.csv("IndividualParametersLaiStudy1Quad1.csv", header = T)
Lai12<-read.csv("IndividualParametersLaiStudy1Quad2.csv", header = T)

colnames(Lai11)[2:ncol(Lai11)]<-paste(colnames(Lai11[2:ncol(Lai11)]),"T1",sep="_")
colnames(Lai12)[2:ncol(Lai12)]<-paste(colnames(Lai12[2:ncol(Lai12)]),"T2",sep="_")

Q.Lai1 <- merge(Lai11, Lai12, by = "session_id", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Lai2/"))
Lai21<-read.csv("IndividualParametersLaiStudy2Quad1.csv", header = T)
Lai22<-read.csv("IndividualParametersLaiStudy2Quad2.csv", header = T)

colnames(Lai21)[2:ncol(Lai21)]<-paste(colnames(Lai21[2:ncol(Lai21)]),"T1",sep="_")
colnames(Lai22)[2:ncol(Lai22)]<-paste(colnames(Lai22[2:ncol(Lai22)]),"T2",sep="_")

Q.Lai2 <- merge(Lai21, Lai22, by = "session_id", all.x = T, all.y = T)

colnames(Q.Cal)[1] <- "subID"
Q.Cal <- cbind(Q.Cal, Study = "Calanchini")
colnames(Q.PI)[1] <- "subID"
Q.PI <- cbind(Q.PI, Study = "PI")
colnames(Q.Gaw)[1] <- "subID"
Q.Gaw <- cbind(Q.Gaw, Study = "Gawronski")
colnames(Q.For)[1] <- "subID"
Q.For <- cbind(Q.For, Study = "Forscher")
colnames(Q.Lai1)[1] <- "subID"
Q.Lai1 <- cbind(Q.Lai1, Study = "Lai 1")
colnames(Q.Lai2)[1] <- "subID"
Q.Lai2 <- cbind(Q.Lai2, Study = "Lai 2")

Quad <- rbind(Q.Cal,Q.PI,Q.Gaw,Q.For,Q.Lai1,Q.Lai2)


Quad$Study[Quad$Study=="PI"]<-"Project Implicit, 2020"
Quad$Study[Quad$Study=="Calanchini"]<-"Wilson & Calanchini, 2022"
Quad$Study[Quad$Study=="Lai 1"]<-"Lai et al., 2016 (Study 1)  "
Quad$Study[Quad$Study=="Lai 2"]<-"Lai et al., 2016 (Study 2)  "
Quad$Study[Quad$Study=="Gawronski"]<-"Gawronski et al., 2017"
Quad$Study[Quad$Study=="Forscher"]<-"Forscher et al., 2017"

path <- "/Volumes/"
setwd(paste0(path,"Research Project/IAT_Retest/Estimates/UCR/"))

Warning: The working directory was changed to /Volumes/Research Project/IAT_Retest/Estimates/UCR inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.

Cal1 <-read.csv("IndividualParametersPDUCR1.csv", header = T)
Cal2 <-read.csv("IndividualParametersPDUCR2.csv", header = T)

colnames(Cal1)[2:ncol(Cal1)]<-paste(colnames(Cal1[2:ncol(Cal1)]),"T1",sep="_")
colnames(Cal2)[2:ncol(Cal2)]<-paste(colnames(Cal2[2:ncol(Cal2)]),"T2",sep="_")

P.Cal <- merge(Cal1, Cal2, by = "subject", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/PI/"))
PI1 <- read.csv("IndividualParametersProjectImplicitPD1.csv")
PI2 <- read.csv("IndividualParametersProjectImplicitPD2.csv")

colnames(PI1)[2:ncol(PI1)]<-paste(colnames(PI1[2:ncol(PI1)]),"T1",sep="_")
colnames(PI2)[2:ncol(PI2)]<-paste(colnames(PI2[2:ncol(PI2)]),"T2",sep="_")

P.PI <- merge(PI1, PI2, by = "ID", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Gawronski/"))
Gaw1 <- read.csv("IndividualParametersPDGawronski1.csv")
Gaw2 <- read.csv("IndividualParametersPDGawronski2.csv")

colnames(Gaw1)[2:ncol(Gaw1)]<-paste(colnames(Gaw1[2:ncol(Gaw1)]),"T1",sep="_")
colnames(Gaw2)[2:ncol(Gaw2)]<-paste(colnames(Gaw2[2:ncol(Gaw2)]),"T2",sep="_")

P.Gaw <- merge(Gaw1, Gaw2, by = "code", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Forscher/"))
For1<-read.csv("IndividualParametersForscherPD1.csv", header = T)
For2<-read.csv("IndividualParametersForscherPD2.csv", header = T)

colnames(For1)[2:ncol(For1)]<-paste(colnames(For1[2:ncol(For1)]),"T1",sep="_")
colnames(For2)[2:ncol(For2)]<-paste(colnames(For2[2:ncol(For2)]),"T2",sep="_")

P.For <- merge(For1, For2, by = "subject", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Lai1/"))
Lai11<-read.csv("IndividualParametersLaiStudy1PD1.csv", header = T)
Lai12<-read.csv("IndividualParametersLaiStudy1PD2.csv", header = T)

colnames(Lai11)[2:ncol(Lai11)]<-paste(colnames(Lai11[2:ncol(Lai11)]),"T1",sep="_")
colnames(Lai12)[2:ncol(Lai12)]<-paste(colnames(Lai12[2:ncol(Lai12)]),"T2",sep="_")

P.Lai1 <- merge(Lai11, Lai12, by = "session_id", all.x = T, all.y = T)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Lai2/"))
Lai21<-read.csv("IndividualParametersLaiStudy2PD1.csv", header = T)
Lai22<-read.csv("IndividualParametersLaiStudy2PD2.csv", header = T)

colnames(Lai21)[2:ncol(Lai21)]<-paste(colnames(Lai21[2:ncol(Lai21)]),"T1",sep="_")
colnames(Lai22)[2:ncol(Lai22)]<-paste(colnames(Lai22[2:ncol(Lai22)]),"T2",sep="_")

P.Lai2 <- merge(Lai21, Lai22, by = "session_id", all.x = T, all.y = T)

colnames(P.Cal)[1] <- "subID"
P.Cal <- cbind(P.Cal, Study = "Calanchini")
colnames(P.PI)[1] <- "subID"
P.PI <- cbind(P.PI, Study = "PI")
colnames(P.Gaw)[1] <- "subID"
P.Gaw <- cbind(P.Gaw, Study = "Gawronski")
colnames(P.For)[1] <- "subID"
P.For <- cbind(P.For, Study = "Forscher")
colnames(P.Lai1)[1] <- "subID"
P.Lai1 <- cbind(P.Lai1, Study = "Lai 1")
colnames(P.Lai2)[1] <- "subID"
P.Lai2 <- cbind(P.Lai2, Study = "Lai 2")

PD <- rbind(P.Cal,P.PI,P.Gaw,P.For,P.Lai1,P.Lai2)

PD$Study[PD$Study=="PI"]<-"Project Implicit, 2020"
PD$Study[PD$Study=="Calanchini"]<-"Wilson & Calanchini, 2022"
PD$Study[PD$Study=="Lai 1"]<-"Lai et al., 2016 (Study 1)  "
PD$Study[PD$Study=="Lai 2"]<-"Lai et al., 2016 (Study 2)  "
PD$Study[PD$Study=="Gawronski"]<-"Gawronski et al., 2017"
PD$Study[PD$Study=="Forscher"]<-"Forscher et al., 2017"

Quad Model

Associate Black-Bad

ACbbScatter <- ggplot(Quad, aes(x=ACbb1_T1, y=ACbb1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Association Black-Bad",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa() 
ACbbScatter

`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"ACbbScatter.jpg"),dpi=600)

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`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"ACbbScatter.tiff"),dpi=600)

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`geom_smooth()` using formula 'y ~ x'

Associate White-Good

ACwgScatter <- ggplot(Quad, aes(x=ACwg1_T1, y=ACwg1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Association White-Good",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) + theme(legend.title = element_text(size = 9)) +
  jtools::theme_apa()
ACwgScatter

`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"ACwgScatter.jpg"),dpi=600)

Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"ACwgScatter.tiff"),dpi=600)

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`geom_smooth()` using formula 'y ~ x'

Detection

DScatter <- ggplot(Quad, aes(x=D1_T1, y=D1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Detection",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) + theme(legend.title = element_text(size = 9)) +
  jtools::theme_apa()
DScatter

`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"DScatter.jpg"),dpi=600)

Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"DScatter.tiff"),dpi=600)

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`geom_smooth()` using formula 'y ~ x'

Overcoming Bias

OBScatter <- ggplot(Quad, aes(x=OB1_T1, y=OB1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Overcoming Bias",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) + theme(legend.title = element_text(size = 9))  +
  jtools::theme_apa()
OBScatter

`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"OBScatter.jpg"),dpi=600)

Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"OBScatter.tiff"),dpi=600)

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`geom_smooth()` using formula 'y ~ x'

Guessing

GScatter <- ggplot(Quad, aes(x=G1_T1, y=G1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Guessing",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
ggsave(paste0(saveto,"GScatter.jpg"),dpi=600)

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`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"GScatter.tiff"),dpi=600)

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`geom_smooth()` using formula 'y ~ x'

Panel

library(ggpubr)
QuadPanel1 <- ggarrange(ACwgScatter, ACbbScatter, DScatter, OBScatter, GScatter,
          ncol = 3, nrow = 2, common.legend = T)

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

QuadPanel1

require(grid)   # for the textGrob() function

QuadPanel2 <- ggarrange(ACwgScatter  + theme(plot.title = element_text(size=12)) + theme(legend.text = element_text(size = 8)) + rremove("ylab") + rremove("xlab"), 
                        ACbbScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        DScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        OBScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        GScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
          ncol = 3, nrow = 2, common.legend = T, labels = NULL, legend = "bottom",
          align = "hv", 
                    font.label = list(size = 10, color = "black", face = "bold", family = NULL, position = "top"))

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

QuadPanel2

annotate_figure(QuadPanel2, left = textGrob("Time 2", rot = 90, vjust = 1, hjust = 0, gp = gpar(cex = 1)),
                    bottom = textGrob("Time 1", vjust = -6.5, gp = gpar(cex = 1))) + theme(legend.position = c(0,-1))
ggsave(paste0(saveto,"QuadPanel.jpg"),dpi=600)

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ggsave(paste0(saveto,"QuadPanel.tiff"),dpi=600)

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PD Model

Automatic Black

AbScatter <- ggplot(PD, aes(x=Ab1_T1, y=Ab1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Automatic Black",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
AbScatter

`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"AbScatter.jpg"),dpi=600)

Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"AbScatter.tiff"),dpi=600)

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`geom_smooth()` using formula 'y ~ x'

Associate White

AwScatter <- ggplot(PD, aes(x=Aw1_T1, y=Aw1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Automatic White",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
AwScatter

`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"AwScatter.jpg"),dpi=600)

Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"AwScatter.tiff"),dpi=600)

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`geom_smooth()` using formula 'y ~ x'

Control Good

CpScatter <- ggplot(PD, aes(x=Cp1_T1, y=Cp1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Control Good",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
CpScatter

`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"CpScatter.jpg"),dpi=600)

Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"CpScatter.tiff"),dpi=600)

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`geom_smooth()` using formula 'y ~ x'

Control Bad

CuScatter <- ggplot(PD, aes(x=Cu1_T1, y=Cu1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Control Bad",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
CuScatter

`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"CuScatter.jpg"),dpi=600)

Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"CuScatter.tiff"),dpi=600)

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`geom_smooth()` using formula 'y ~ x'

Control White

CwScatter <- ggplot(PD, aes(x=Cw1_T1, y=Cw1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Control White",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
CwScatter

`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"CwScatter.jpg"),dpi=600)

Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"CwScatter.tiff"),dpi=600)

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`geom_smooth()` using formula 'y ~ x'

Control Black

CbScatter <- ggplot(PD, aes(x=Cb1_T1, y=Cb1_T2, color=Study)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Control Black",
       x="Time 1", y = "Time 2") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
CbScatter

`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"CbScatter.jpg"),dpi=600)

Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"CbScatter.tiff"),dpi=600)

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`geom_smooth()` using formula 'y ~ x'

Panel

library(ggpubr)
PDPanel1 <- ggarrange(AwScatter, AbScatter, CuScatter, CpScatter, CbScatter, CwScatter,
          ncol = 3, nrow = 2, common.legend = T)

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

PDPanel1

require(grid)   # for the textGrob() function

PDPanel2 <- ggarrange(AwScatter  + theme(plot.title = element_text(size=12))  + theme(legend.text = element_text(size = 8)) + rremove("ylab") + rremove("xlab"), 
                        AbScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        CuScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        CpScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        CbScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        CwScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
          ncol = 3, nrow = 2, common.legend = T, labels = NULL, legend = "bottom",
          align = "hv", 
                    font.label = list(size = 10, color = "black", face = "bold", family = NULL, position = "top"))

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

PDPanel2 <- annotate_figure(PDPanel2, left = textGrob("Time 2", rot = 90, vjust = 1, hjust = 0, gp = gpar(cex = 1)),
                    bottom = textGrob("Time 1", vjust = -6.5, gp = gpar(cex = 1))) + theme(legend.position = c(0,-1))
PDPanel2
ggsave(paste0(saveto,"PDPanel.jpg"),dpi=600)

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ggsave(paste0(saveto,"PDPanel.tiff"),dpi=600)

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Reliability Meta-Analysis

dt <- read.csv("~/Documents/GitHub/MPT-Reliability/Retest/output/combQuad_Df.csv")[2:6]

dt$Param[dt$Param=="D"]<-"Detection"
dt$Param[dt$Param=="OB"]<-"Overcoming Bias"
dt$Param[dt$Param=="ACbb"]<-"Association Black-Bad"
dt$Param[dt$Param=="ACwg"]<-"Association White-Good"
dt$Param[dt$Param=="G"]<-"Guessing"

dt$Param[dt$Param=="PI"]<-"Project Implicit, 2020"
dt$Param[dt$Param=="Calanchini"]<-"Wilson & Calanchini, 2022"
dt$Param[dt$Param=="Lai1"]<-"Lai et al., 2016 (Study 1)  "
dt$Param[dt$Param=="Lai2"]<-"Lai et al., 2016 (Study 2)  "
dt$Param[dt$Param=="Gawronski"]<-"Gawronski et al., 2017"
dt$Param[dt$Param=="Forscher"]<-"Forscher et al., 2017"

# indent the subgroup if there is a number in the placebo column
dt$Param <- ifelse(dt$type==0, 
                      dt$Param,
                      paste0(" ", dt$Param))

colnames(dt)[colnames(dt)=="Param"] <- "   "
dt$` ` <- paste(rep(" ", 20), collapse = " ")
dt$`ICC (95% CI)` <- ifelse(is.na(dt$ICC), "",
                             sprintf("%.2f [%.2f, %.2f]",
                                     dt$ICC, dt$CI.LB, dt$CI.UB))


dt$ICCstudy <- ifelse(dt$type==1, dt$ICC, NA)
dt$ICC.LBstudy <- ifelse(dt$type==1, dt$CI.LB, NA)
dt$ICC.UBstudy <- ifelse(dt$type==1, dt$CI.UB, NA)
dt$Sizestudy <- ifelse(dt$type==1, .6, NA)
dt$Sizemeta <- ifelse(dt$type==0, 1.2, NA)
dt$ICCmeta <- ifelse(dt$type==0, dt$ICC, NA)
dt$ICC.LBmeta <- ifelse(dt$type==0, dt$CI.LB, NA)
dt$ICC.UBmeta <- ifelse(dt$type==0, dt$CI.UB, NA)

#dt<-dt[c(1:4,6:7)]
dt<-dt[c(1:4,6:ncol(dt))]
colnames(dt)[colnames(dt)=="ICC (95% CI)"] <- "     "

library(forestploter)
# p <- forest(
#   dt[c(1,5,6)],
#             est = dt$ICC,
#             lower = dt$CI.LB, 
#             upper = dt$CI.UB,
#             ci_column = 2,
#             ref_line = .4,
#             xlim = c(-.1, 1),
#             ticks_at = c(0, .4,.6, .75, 1),
#   sizes = .1
# )

tm <- forest_theme(
                   ci_pch = c(15, 16),
                   ci_col = c("black","red"),
                   ci_lty = c( "solid", "dashed"),
                  # footnote_col = "blue",
                   legend_name = " ",
                  legend_position = "bottom",
                   legend_value = c("Meta-Analysis ", "Study")
                  
                  )

p <- forest(
  dt[c(1,5,6)],
            est = list(dt$ICCmeta, dt$ICCstudy),
            lower = list(dt$ICC.LBmeta, dt$ICC.LBstudy), 
            upper = list(dt$ICC.UBmeta, dt$ICC.UBstudy),
              ci_column = 2,
            ref_line = .4,
            xlim = c(-.1, 1),
            ticks_at = c(0, .4,.6, .75, 1),
  sizes = list(dt$Sizemeta, dt$Sizestudy),
  theme = tm
)

library(grid)
p <- edit_plot(p,
               row = seq(from=1,to=nrow(dt),by=7),
               gp = gpar(fontface = c("bold","italic")
                         ), col = 1)

studyInds <- setdiff(1:nrow(dt),seq(from=1,to=nrow(dt),by=7))
paramInds <- seq(from=1,to=nrow(dt),by=7)

p <- edit_plot(p, row = studyInds, which = "background",
               gp = gpar(fill = "white"))
p <- edit_plot(p, row = paramInds, which = "background",
               gp = gpar(fill = "grey"))

png(paste0(saveto,"Quad_TRT.png"), units="in", width=10, height=12, res=300)
plot(p)
dev.off()

null device 
          1 

# ggsave function
ggplot2::ggsave(paste0(saveto,"Quad_TRT.png"),
                dpi = 300,
                width = 6, height = 11, units = "in", plot=p)

ggplot2::ggsave(paste0(saveto,"Quad_TRT.tiff"),
                dpi = 600,
                width = 6, height = 11, units = "in", plot=p)

dt <- read.csv("~/Documents/GitHub/MPT-Reliability/Retest/output/combPD_Df.csv")[2:6]

dt$Param[dt$Param=="Ab"]<-"Automatic Black"
dt$Param[dt$Param=="Aw"]<-"Automatic White"
dt$Param[dt$Param=="Cb"]<-"Control Black"
dt$Param[dt$Param=="Cw"]<-"Control White"
dt$Param[dt$Param=="Cu"]<-"Control Bad"
dt$Param[dt$Param=="Cp"]<-"Control Good"

dt$Param[dt$Param=="PI"]<-"Project Implicit, 2020"
dt$Param[dt$Param=="Calanchini"]<-"Wilson & Calanchini, 2022"
dt$Param[dt$Param=="Lai1"]<-"Lai et al., 2016 (Study 1)  "
dt$Param[dt$Param=="Lai2"]<-"Lai et al., 2016 (Study 2)  "
dt$Param[dt$Param=="Gawronski"]<-"Gawronski et al., 2017"
dt$Param[dt$Param=="Forscher"]<-"Forscher et al., 2017"

# indent the subgroup if there is a number in the placebo column
dt$Param <- ifelse(dt$type==0, 
                      dt$Param,
                      paste0(" ", dt$Param))

colnames(dt)[colnames(dt)=="Param"] <- "   "
colnames(dt)[colnames(dt)=="Param"] <- "   "
dt$` ` <- paste(rep(" ", 20), collapse = " ")
dt$`ICC (95% CI)` <- ifelse(is.na(dt$ICC), "",
                             sprintf("%.2f [%.2f, %.2f]",
                                     dt$ICC, dt$CI.LB, dt$CI.UB))

dt$ICCstudy <- ifelse(dt$type==1, dt$ICC, NA)
dt$ICC.LBstudy <- ifelse(dt$type==1, dt$CI.LB, NA)
dt$ICC.UBstudy <- ifelse(dt$type==1, dt$CI.UB, NA)
dt$Sizestudy <- ifelse(dt$type==1, .6, NA)
dt$Sizemeta <- ifelse(dt$type==0, 1.2, NA)
dt$ICCmeta <- ifelse(dt$type==0, dt$ICC, NA)
dt$ICC.LBmeta <- ifelse(dt$type==0, dt$CI.LB, NA)
dt$ICC.UBmeta <- ifelse(dt$type==0, dt$CI.UB, NA)

#dt<-dt[c(1:4,6:7)]
dt<-dt[c(1:4,6:ncol(dt))]
colnames(dt)[colnames(dt)=="ICC (95% CI)"] <- "     "

library(forestploter)
# p <- forest(
#   dt[c(1,5,6)],
#             est = dt$ICC,
#             lower = dt$CI.LB, 
#             upper = dt$CI.UB,
#             ci_column = 2,
#             ref_line = .4,
#             xlim = c(-.1, 1),
#             ticks_at = c(0, .4,.6, .75, 1),
#   sizes = .1
# )

tm <- forest_theme(
                   ci_pch = c(15, 16),
                   ci_col = c("black","red"),
                   ci_lty = c( "solid", "dashed"),
                  # footnote_col = "blue",
                   legend_name = " ",
                  legend_position = "bottom",
                   legend_value = c("Meta-Analysis ", "Study")
                  
                  )

p <- forest(
  dt[c(1,5,6)],
            est = list(dt$ICCmeta, dt$ICCstudy),
            lower = list(dt$ICC.LBmeta, dt$ICC.LBstudy), 
            upper = list(dt$ICC.UBmeta, dt$ICC.UBstudy),
              ci_column = 2,
            ref_line = .4,
            xlim = c(-.1, 1),
            ticks_at = c(0, .4,.6, .75, 1),
  sizes = list(dt$Sizemeta, dt$Sizestudy),
  theme = tm
)

library(grid)
p <- edit_plot(p,
               row = seq(from=1,to=nrow(dt),by=7),
               gp = gpar(fontface = c("bold","italic")
                         ), col = 1)

studyInds <- setdiff(1:nrow(dt),seq(from=1,to=nrow(dt),by=7))
paramInds <- seq(from=1,to=nrow(dt),by=7)

p <- edit_plot(p, row = studyInds, which = "background",
               gp = gpar(fill = "white"))
p <- edit_plot(p, row = paramInds, which = "background",
               gp = gpar(fill = "grey"))

png(paste0(saveto,"PDP_TRT.png"), units="in", width=10, height=15, res=300)
plot(p)
dev.off()

null device 
          1 

# ggsave function
ggplot2::ggsave(paste0(saveto,"PDP_TRT.png"),
                dpi = 300,
                width = 6, height = 13, units = "in", plot=p)

ggplot2::ggsave(paste0(saveto,"PDP_TRT.tiff"),
                dpi = 600,
                width = 6, height = 13, units = "in", plot=p)

Recovery

setwd(paste0("/Volumes/Research Project/IAT_Retest/Recovery/RecoveredParams/PD/"))

Warning: The working directory was changed to /Volumes/Research Project/IAT_Retest/Recovery/RecoveredParams/PD inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.

times<-c("Time1","Time2")

allrecovs <- as.data.frame(matrix(ncol=14,nrow=0))

PDdirs <- list.dirs(path = ".", full.names = TRUE, recursive = TRUE)[-1]
for(d in PDdirs){
  for(ti in times){
    curFiles <- list.files(path=d,pattern="\\.csv$")
    id<-grep(ti,curFiles)
    output<-read.csv(paste0(d,"/",curFiles[id]))
    name<-gsub("./","",d)
    name<-gsub(" ","",name)
    
    if(ti=="Time1"){
      #colnames(output) <- paste(colnames(output),"T1",sep="_")
      output$time <- "T1"
    }else if(ti=="Time2"){
      #colnames(output) <- paste(colnames(output),"T2",sep="_")
      output$time <- "T2"
    }
    
    assign(paste0(name,".PD.Recov.",ti),output)
  }
  df <- rbind(get(paste0(name,".PD.Recov.Time1")),get(paste0(name,".PD.Recov.Time2")))
  assign(paste0(name,".PD.Recov"),df)
  allrecovs <- rbind(allrecovs,cbind(df,Study=name))
}


allrecovs$Study <- gsub("Lai1", "Lai 1", allrecovs$Study)
allrecovs$Study <- gsub("Lai2", "Lai 2", allrecovs$Study)
PD.recovs <- allrecovs
PD.recovs <- dplyr::rename(PD.recovs, subID=X)

tapply(PD.recovs$Ab1, PD.recovs$Study, function(x) length(x))

Calanchini   Forscher  Gawronski      Lai 1      Lai 2         PI 
       210         64        232        160        926       2480 

path <- "/Volumes/"
setwd(paste0(path,"Research Project/IAT_Retest/Estimates/UCR/"))

Warning: The working directory was changed to /Volumes/Research Project/IAT_Retest/Estimates/UCR inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.

Cal1 <-read.csv("IndividualParametersPDUCR1.csv", header = T)
Cal2 <-read.csv("IndividualParametersPDUCR2.csv", header = T)

Cal1$time <- "T1"
Cal2$time <- "T2"
Cal1$subID <- 1:nrow(Cal1)
Cal2$subID <- 1:nrow(Cal2)
P.Cal <- rbind(Cal1, Cal2)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/PI/"))
PI1 <- read.csv("IndividualParametersProjectImplicitPD1.csv")
PI2 <- read.csv("IndividualParametersProjectImplicitPD2.csv")

PI1$time <- "T1"
PI2$time <- "T2"
PI1$subID <- 1:nrow(PI1)
PI2$subID <- 1:nrow(PI2)
P.PI <- rbind(PI1, PI2)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Gawronski/"))
Gaw1 <- read.csv("IndividualParametersPDGawronski1.csv")
Gaw2 <- read.csv("IndividualParametersPDGawronski2.csv")

Gaw1$time <- "T1"
Gaw2$time <- "T2"
Gaw1$subID <- 1:nrow(Gaw1)
Gaw2$subID <- 1:nrow(Gaw2)
P.Gaw <- rbind(Gaw1, Gaw2)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Forscher/"))
For1<-read.csv("IndividualParametersForscherPD1.csv", header = T)
For2<-read.csv("IndividualParametersForscherPD2.csv", header = T)

For1$time <- "T1"
For2$time <- "T2"
For1$subID <- 1:nrow(For1)
For2$subID <- 1:nrow(For2)
P.For <- rbind(For1, For2)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Lai1/"))
Lai11<-read.csv("IndividualParametersLaiStudy1PD1.csv", header = T)
Lai12<-read.csv("IndividualParametersLaiStudy1PD2.csv", header = T)

Lai11$time <- "T1"
Lai12$time <- "T2"
Lai11$subID <- 1:nrow(Lai11)
Lai12$subID <- 1:nrow(Lai12)
P.Lai1 <- rbind(Lai11, Lai12)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Lai2/"))
Lai21<-read.csv("IndividualParametersLaiStudy2PD1.csv", header = T)
Lai22<-read.csv("IndividualParametersLaiStudy2PD2.csv", header = T)

Lai21$time <- "T1"
Lai22$time <- "T2"
Lai21$subID <- 1:nrow(Lai21)
Lai22$subID <- 1:nrow(Lai22)
P.Lai2 <- rbind(Lai21,Lai22)

colnames(P.Cal)[1] <- "subID"
P.Cal <- cbind(P.Cal, Study = "Calanchini")
colnames(P.PI)[1] <- "subID"
P.PI <- cbind(P.PI, Study = "PI")
colnames(P.Gaw)[1] <- "subID"
P.Gaw <- cbind(P.Gaw, Study = "Gawronski")
colnames(P.For)[1] <- "subID"
P.For <- cbind(P.For, Study = "Forscher")
colnames(P.Lai1)[1] <- "subID"
P.Lai1 <- cbind(P.Lai1, Study = "Lai 1")
colnames(P.Lai2)[1] <- "subID"
P.Lai2 <- cbind(P.Lai2, Study = "Lai 2")

PD.orig <- rbind(P.Cal,P.PI,P.Gaw,P.For,P.Lai1,P.Lai2)
PD.orig[1] <- NULL

colnames(PD.recovs)[2: (ncol(PD.recovs)-2) ] <- paste(colnames(PD.recovs)[2: (ncol(PD.recovs)-2) ],"R",sep="_")
colnames(PD.orig)[1: (ncol(PD.orig)-3) ] <- paste(colnames(PD.orig)[1: (ncol(PD.orig)-3) ],"O",sep="_")
PD.orig <- PD.orig[order(PD.orig$Study, PD.orig$time),]
PD.recovs <- PD.recovs[order(PD.recovs$Study, PD.recovs$time),]

#PD.orig.recovs <- cbind(PD.orig,PD.recovs)
PD.orig.recovs <- merge(PD.orig, PD.recovs, by = c("subID","Study","time"))

PD.orig.recovs$Study[PD.orig.recovs$Study=="PI"]<-"Project Implicit, 2020"
PD.orig.recovs$Study[PD.orig.recovs$Study=="Calanchini"]<-"Wilson & Calanchini, 2022"
PD.orig.recovs$Study[PD.orig.recovs$Study=="Lai 1"]<-"Lai et al., 2016 (Study 1)  "
PD.orig.recovs$Study[PD.orig.recovs$Study=="Lai 2"]<-"Lai et al., 2016 (Study 2)  "
PD.orig.recovs$Study[PD.orig.recovs$Study=="Gawronski"]<-"Gawronski et al., 2017"
PD.orig.recovs$Study[PD.orig.recovs$Study=="Forscher"]<-"Forscher et al., 2017"

Recovery

PD Model

Automatic Black

AbScatter <- ggplot(PD.orig.recovs, aes(x=Ab1_O, y=Ab1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype = "dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Automatic Black",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()

AbScatter

`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"AbScatterREC.jpg"),dpi=600)

Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"AbScatterREC.tiff"),dpi=600)

Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

Automatic White

AwScatter <- ggplot(PD.orig.recovs, aes(x=Aw1_O, y=Aw1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Automatic White",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
AwScatter

`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"AwScatterREC.jpg"),dpi=600)

Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"AwScatterREC.tiff"),dpi=600)

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`geom_smooth()` using formula 'y ~ x'

Control Good

CpScatter <- ggplot(PD.orig.recovs, aes(x=Cp1_O, y=Cp1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Control Good",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
CpScatter

`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"CpScatterREC.jpg"),dpi=600)

Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"AwScatterREC.tiff"),dpi=600)

Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

Control Bad

CuScatter <- ggplot(PD.orig.recovs, aes(x=Cu1_O, y=Cu1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Control Bad",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
CuScatter

`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"CuScatterREC.jpg"),dpi=600)

Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"CuScatterREC.tiff"),dpi=600)

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`geom_smooth()` using formula 'y ~ x'

Control White

CwScatter <- ggplot(PD.orig.recovs, aes(x=Cw1_O, y=Cw1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Control White",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
CwScatter

`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"CwScatterREC.jpg"),dpi=600)

Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"CwScatterREC.tiff"),dpi=600)

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`geom_smooth()` using formula 'y ~ x'

Control Black

CbScatter <- ggplot(PD.orig.recovs, aes(x=Cb1_O, y=Cb1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Control Black",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
CbScatter

`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"CbScatterREC.jpg"),dpi=600)

Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"CbScatterREC.tiff"),dpi=600)

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`geom_smooth()` using formula 'y ~ x'

Panel

library(ggpubr)
PDPanel1 <- ggarrange(AwScatter, AbScatter, CuScatter, CpScatter, CbScatter, CwScatter,
          ncol = 3, nrow = 2, common.legend = T)

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

PDPanel1

require(grid)   # for the textGrob() function

PDPanel2 <- ggarrange(AwScatter  + theme(plot.title = element_text(size=12)) + theme(legend.text = element_text(size = 8)) + rremove("ylab") + rremove("xlab"), 
                        AbScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        CuScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        CpScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        CbScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        CwScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
          ncol = 3, nrow = 2, common.legend = T, labels = NULL, legend = c("bottom"),
          align = "hv", 
                    font.label = list(size = 10, color = "black", face = "bold", family = NULL, position = "top"))

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

PDPanel2 <- annotate_figure(PDPanel2, left = textGrob("Recovered", rot = 90, vjust = 1, hjust = 0, gp = gpar(cex = 1)),
                    bottom = textGrob("Original", vjust = -6.5, gp = gpar(cex = 1))) + theme(legend.position = c(0,-1))
PDPanel2
ggsave(paste0(saveto,"PDPanelREC.jpg"),dpi=600)

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ggsave(paste0(saveto,"PDPanelREC.tiff"),dpi=600)

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Quad Model

Recovery

setwd(paste0("/Volumes/Research Project/IAT_Retest/Recovery/RecoveredParams/Quad/"))

Warning: The working directory was changed to /Volumes/Research Project/IAT_Retest/Recovery/RecoveredParams/Quad inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.

times<-c("Time1","Time2")

allrecovs <- as.data.frame(matrix(ncol=14,nrow=0))

Quaddirs <- list.dirs(path = ".", full.names = TRUE, recursive = TRUE)[-1]
for(d in Quaddirs){
  for(ti in times){
    curFiles <- list.files(path=d,pattern="\\.csv$")
    id<-grep(ti,curFiles)
    output<-read.csv(paste0(d,"/",curFiles[id]))
    name<-gsub("./","",d)
    name<-gsub(" ","",name)
    
    if(ti=="Time1"){
      #colnames(output) <- paste(colnames(output),"T1",sep="_")
      output$time <- "T1"
    }else if(ti=="Time2"){
      #colnames(output) <- paste(colnames(output),"T2",sep="_")
      output$time <- "T2"
    }
    
    assign(paste0(name,".Quad.Recov.",ti),output)
  }
  df <- rbind(get(paste0(name,".Quad.Recov.Time1")),get(paste0(name,".Quad.Recov.Time2")))
  assign(paste0(name,".Quad.Recov"),df)
  allrecovs <- rbind(allrecovs,cbind(df,Study=name))
}


allrecovs$Study <- gsub("Lai1", "Lai 1", allrecovs$Study)
allrecovs$Study <- gsub("Lai2", "Lai 2", allrecovs$Study)
Quad.recovs <- allrecovs
Quad.recovs <- dplyr::rename(Quad.recovs, subID=X)

tapply(Quad.recovs$ACbb1, Quad.recovs$Study, function(x) length(x))

Calanchini   Forscher  Gawronski      Lai 1      Lai 2         PI 
       210         64        232        160        926       2480 

path <- "/Volumes/"
setwd(paste0(path,"Research Project/IAT_Retest/Estimates/UCR/"))

Warning: The working directory was changed to /Volumes/Research Project/IAT_Retest/Estimates/UCR inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.

Cal1 <-read.csv("IndividualParametersQuadUCR1.csv", header = T)
Cal2 <-read.csv("IndividualParametersQuadUCR2.csv", header = T)

Cal1$time <- "T1"
Cal2$time <- "T2"
Cal1$subID <- 1:nrow(Cal1)
Cal2$subID <- 1:nrow(Cal2)
P.Cal <- rbind(Cal1, Cal2)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/PI/"))
PI1 <- read.csv("IndividualParametersQuadProjectImplicit1.csv")
PI2 <- read.csv("IndividualParametersQuadProjectImplicit2.csv")

PI1$time <- "T1"
PI2$time <- "T2"
PI1$subID <- 1:nrow(PI1)
PI2$subID <- 1:nrow(PI2)
P.PI <- rbind(PI1, PI2)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Gawronski/"))
Gaw1 <- read.csv("IndividualParametersQuadGawronski1.csv")
Gaw2 <- read.csv("IndividualParametersQuadGawronski2.csv")

Gaw1$time <- "T1"
Gaw2$time <- "T2"
Gaw1$subID <- 1:nrow(Gaw1)
Gaw2$subID <- 1:nrow(Gaw2)
P.Gaw <- rbind(Gaw1, Gaw2)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Forscher/"))
For1<-read.csv("IndividualParametersQuadForscher1.csv", header = T)
For2<-read.csv("IndividualParametersQuadForscher2.csv", header = T)

For1$time <- "T1"
For2$time <- "T2"
For1$subID <- 1:nrow(For1)
For2$subID <- 1:nrow(For2)
P.For <- rbind(For1, For2)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Lai1/"))
Lai11<-read.csv("IndividualParametersLaiStudy1Quad1.csv", header = T)
Lai12<-read.csv("IndividualParametersLaiStudy1Quad2.csv", header = T)

Lai11$time <- "T1"
Lai12$time <- "T2"
Lai11$subID <- 1:nrow(Lai11)
Lai12$subID <- 1:nrow(Lai12)
P.Lai1 <- rbind(Lai11, Lai12)

setwd(paste0(path,"Research Project/IAT_Retest/Estimates/Lai2/"))
Lai21<-read.csv("IndividualParametersLaiStudy2Quad1.csv", header = T)
Lai22<-read.csv("IndividualParametersLaiStudy2Quad2.csv", header = T)

Lai21$time <- "T1"
Lai22$time <- "T2"
Lai21$subID <- 1:nrow(Lai21)
Lai22$subID <- 1:nrow(Lai22)
P.Lai2 <- rbind(Lai21,Lai22)

colnames(P.Cal)[1] <- "subID"
P.Cal <- cbind(P.Cal, Study = "Calanchini")
colnames(P.PI)[1] <- "subID"
P.PI <- cbind(P.PI, Study = "PI")
colnames(P.Gaw)[1] <- "subID"
P.Gaw <- cbind(P.Gaw, Study = "Gawronski")
colnames(P.For)[1] <- "subID"
P.For <- cbind(P.For, Study = "Forscher")
colnames(P.Lai1)[1] <- "subID"
P.Lai1 <- cbind(P.Lai1, Study = "Lai 1")
colnames(P.Lai2)[1] <- "subID"
P.Lai2 <- cbind(P.Lai2, Study = "Lai 2")

Quad.orig <- rbind(P.Cal,P.PI,P.Gaw,P.For,P.Lai1,P.Lai2)
Quad.orig[1] <- NULL

colnames(Quad.recovs)[2: (ncol(Quad.recovs)-2) ] <- paste(colnames(Quad.recovs)[2: (ncol(Quad.recovs)-2) ],"R",sep="_")
colnames(Quad.orig)[1: (ncol(Quad.orig)-3) ] <- paste(colnames(Quad.orig)[1: (ncol(Quad.orig)-3) ],"O",sep="_")
Quad.orig <- Quad.orig[order(Quad.orig$Study, Quad.orig$time),]
Quad.recovs <- Quad.recovs[order(Quad.recovs$Study, Quad.recovs$time),]

#Quad.orig.recovs <- cbind(Quad.orig,Quad.recovs)
Quad.orig.recovs <- merge(Quad.orig, Quad.recovs, by = c("subID","Study","time"))

Quad.orig.recovs$Study[Quad.orig.recovs$Study=="PI"]<-"Project Implicit, 2020"
Quad.orig.recovs$Study[Quad.orig.recovs$Study=="Calanchini"]<-"Wilson & Calanchini, 2022"
Quad.orig.recovs$Study[Quad.orig.recovs$Study=="Lai 1"]<-"Lai et al., 2016 (Study 1)  "
Quad.orig.recovs$Study[Quad.orig.recovs$Study=="Lai 2"]<-"Lai et al., 2016 (Study 2)  "
Quad.orig.recovs$Study[Quad.orig.recovs$Study=="Gawronski"]<-"Gawronski et al., 2017"
Quad.orig.recovs$Study[Quad.orig.recovs$Study=="Forscher"]<-"Forscher et al., 2017"

Quad Model

Associate Black-Bad

ACbbScatter <- ggplot(Quad.orig.recovs, aes(x=ACbb1_O, y=ACbb1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Association Black-Bad",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
ACbbScatter

`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"ACbbScatterREC.jpg"),dpi=600)

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`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"ACbbScatterREC.tiff"),dpi=600)

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`geom_smooth()` using formula 'y ~ x'

Associate White-Good

ACwgScatter <- ggplot(Quad.orig.recovs, aes(x=ACwg1_O, y=ACwg1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Association White-Good",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
ACwgScatter

`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"ACwgScatterREC.jpg"),dpi=600)

Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"ACwgScatterREC.tiff"),dpi=600)

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`geom_smooth()` using formula 'y ~ x'

Detection

DScatter <- ggplot(Quad.orig.recovs, aes(x=D1_O, y=D1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Detection",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
DScatter

`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"DScatterREC.jpg"),dpi=600)

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`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"DScatterREC.tiff"),dpi=600)

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`geom_smooth()` using formula 'y ~ x'

Overcoming Bias

OBScatter <- ggplot(Quad.orig.recovs, aes(x=OB1_O, y=OB1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Overcoming Bias",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
OBScatter

`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"OBScatterREC.jpg"),dpi=600)

Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"OBScatterREC.tiff"),dpi=600)

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`geom_smooth()` using formula 'y ~ x'

Guessing

GScatter <- ggplot(Quad.orig.recovs, aes(x=G1_O, y=G1_R, color=Study, shape=time)) + 
  geom_point(alpha = .2)+
  geom_smooth(method=lm, se=TRUE, linetype="dashed",
             color="grey")  + geom_abline(intercept = 0, slope = 1) + ylim(0,1) + xlim(0,1) + labs(title="Guessing",
       x="Original", y = "Recovered") + guides(colour = guide_legend(override.aes = list(alpha = 1))) +
  jtools::theme_apa()
GScatter

`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"GScatter.jpg"),dpi=600)

Saving 7.29 x 4.51 in image
`geom_smooth()` using formula 'y ~ x'

ggsave(paste0(saveto,"GScatter.tiff"),dpi=600)

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`geom_smooth()` using formula 'y ~ x'

Panel

library(ggpubr)
QuadPanel1 <- ggarrange(ACwgScatter, ACbbScatter, DScatter, OBScatter, GScatter,
          ncol = 3, nrow = 2, common.legend = T)

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

QuadPanel1

require(grid)   # for the textGrob() function

QuadPanel2 <- ggarrange(ACwgScatter  + theme(plot.title = element_text(size=12))  + theme(legend.text = element_text(size = 8)) + rremove("ylab") + rremove("xlab"), 
                        ACbbScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        DScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        OBScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
                        GScatter  + theme(plot.title = element_text(size=12)) + rremove("ylab") + rremove("xlab"),
          ncol = 3, nrow = 2, common.legend = T, labels = NULL, legend = "bottom",
          align = "hv", 
                    font.label = list(size = 10, color = "black", face = "bold", family = NULL, position = "top"))

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

QuadPanel2

annotate_figure(QuadPanel2, left = textGrob("Recovered", rot = 90, vjust = 1, hjust = 0, gp = gpar(cex = 1)),
                    bottom = textGrob("Original", vjust = -6.5, gp = gpar(cex = 1))) + theme(legend.position = c(0,-1))
ggsave(paste0(saveto,"QuadPanelREC.jpg"),dpi=600)

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ggsave(paste0(saveto,"QuadPanelREC.tiff"),dpi=600)

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Recovery Meta-Analysis

Quad

dt <- read.csv("~/Documents/GitHub/MPT-Reliability/Recovery/output/combQuad_Recovery_Df.csv")[2:7]
dt$id <- unlist(lapply(1:5, function(x) rep(x,times=13)))

dt$Param[dt$Param=="D"]<-"Detection"
dt$Param[dt$Param=="OB"]<-"Overcoming Bias"
dt$Param[dt$Param=="ACbb"]<-"Association Black-Bad"
dt$Param[dt$Param=="ACwg"]<-"Association White-Good"
dt$Param[dt$Param=="G"]<-"Guessing"

dt$Param[dt$Param=="PI"]<-"Project Implicit, 2020"
dt$Param[dt$Param=="Calanchini"]<-"Wilson & Calanchini, 2022"
dt$Param[dt$Param=="Lai1"]<-"Lai et al., 2016 (Study 1)  "
dt$Param[dt$Param=="Lai2"]<-"Lai et al., 2016 (Study 2)  "
dt$Param[dt$Param=="Gawronski"]<-"Gawronski et al., 2017"
dt$Param[dt$Param=="Forscher"]<-"Forscher et al., 2017"

# indent the subgroup if there is a number in the placebo column
dt$Param <- ifelse(dt$type==0, 
                      dt$Param,
                      paste0(" ", dt$Param))

dt$` ` <- paste(rep(" ", 20), collapse = " ")
dt$`Recovery (95% CI)` <- ifelse(is.na(dt$Recov), "",
                             sprintf("%.2f [%.2f, %.2f]",
                                     dt$Recov, dt$CI.LB, dt$CI.UB))

dt2 <- pivot_wider(dt, id_cols = c("Param","id"),  names_from = c(Time), values_from = c(Recov,CI.LB,CI.UB,`Recovery (95% CI)`) )
dt2$`Recovery [95% CI]` <- ifelse(!is.na(dt2$`Recovery (95% CI)_Meta`), paste0("                 ",dt2$`Recovery (95% CI)_Meta`), paste0("T1 :", paste0(dt2$`Recovery (95% CI)_Time1`), " ; ", "T2: ", paste0(dt2$`Recovery (95% CI)_Time2`)))
dt2$` ` <- paste(rep(" ", 20), collapse = " ")


dt3<-dt2 %>% select(Param,Recov_Meta:11,15,16)

colnames(dt3)[colnames(dt3)=="Param"] <- "   "
colnames(dt3)[colnames(dt3)=="Recovery [95% CI]"] <- "    "

dt3$size_Meta <- ifelse(is.na(dt3$Recov_Meta), NA, 1.2)
dt3$size_T1 <- ifelse(is.na(dt3$Recov_Time1), NA, .6)
dt3$size_T2 <- ifelse(is.na(dt3$Recov_Time2), NA, .6)

# Set-up theme
tm <- forest_theme(
                   ci_pch = c(15, 16, 17),
                   ci_col = c("black","red","blue"),
                   ci_lty = c( "solid", "dashed", "dotted"),
                  # footnote_col = "blue",
                   legend_name = " ",
                  legend_position = "bottom",
                   legend_value = c("Meta-Analysis ", "Time 1 ", "Time 2 "))

library(forestploter)
p <- forest(
  dt3[c(1,12,11)],
            est = list(dt3$Recov_Meta, dt3$Recov_Time1, dt3$Recov_Time2),
            lower = list(dt3$CI.LB_Meta, dt3$CI.LB_Time1, dt3$CI.LB_Time2), 
            upper = list(dt3$CI.UB_Meta, dt3$CI.UB_Time1, dt3$CI.UB_Time2),
            ci_column = 2,
            ref_line = .7,
            xlim = c(-.1, 1),
            ticks_at = c(0, .2, .5, .7, 1),
  sizes = list(dt3$size_Meta, dt3$size_T1, dt3$size_T2),
  theme = tm
)
plot(p)

library(grid)
p <- edit_plot(p,
               row = seq(from=1,to=nrow(dt3),by=7),
               gp = gpar(fontface = c("bold","italic")
                         ), col = 1)

studyInds <- setdiff(1:nrow(dt3),seq(from=1,to=nrow(dt3),by=7))
paramInds <- seq(from=1,to=nrow(dt),by=7)

p <- edit_plot(p, row = studyInds, which = "background",
               gp = gpar(fill = "white"))
p <- edit_plot(p, row = paramInds, which = "background",
               gp = gpar(fill = "grey"))

png("~/Desktop/Quad_RecovForest.png", units="in", width=10, height=15, res=300)
plot(p)
dev.off()

null device 
          1 

# ggsave function
ggplot2::ggsave(paste0(saveto,"Quad_RecovForest.png"),
                dpi = 300,
                width = 8, height = 15, units = "in", plot=p)

ggplot2::ggsave(paste0(saveto,"Quad_RecovForest.tiff"),
                dpi = 300,
                width = 8, height = 15, units = "in", plot=p)

dt <- read.csv("~/Documents/GitHub/MPT-Reliability/Recovery/output/combPD_Recovery_Df.csv")[2:7]
dt$id <- unlist(lapply(1:6, function(x) rep(x,times=13)))

dt$Param[dt$Param=="Ab"]<-"Automatic Black"
dt$Param[dt$Param=="Aw"]<-"Automatic White"
dt$Param[dt$Param=="Cb"]<-"Control Black"
dt$Param[dt$Param=="Cw"]<-"Control White"
dt$Param[dt$Param=="Cu"]<-"Control Bad"
dt$Param[dt$Param=="Cp"]<-"Control Good"

dt$Param[dt$Param=="PI"]<-"Project Implicit, 2020"
dt$Param[dt$Param=="Calanchini"]<-"Wilson & Calanchini, 2022"
dt$Param[dt$Param=="Lai1"]<-"Lai et al., 2016 (Study 1)  "
dt$Param[dt$Param=="Lai2"]<-"Lai et al., 2016 (Study 2)  "
dt$Param[dt$Param=="Gawronski"]<-"Gawronski et al., 2017"
dt$Param[dt$Param=="Forscher"]<-"Forscher et al., 2017"

# indent the subgroup if there is a number in the placebo column
dt$Param <- ifelse(dt$type==0, 
                      dt$Param,
                      paste0(" ", dt$Param))

dt$` ` <- paste(rep(" ", 20), collapse = " ")
dt$`Recovery (95% CI)` <- ifelse(is.na(dt$Recov), "",
                             sprintf("%.2f [%.2f, %.2f]",
                                     dt$Recov, dt$CI.LB, dt$CI.UB))

dt2 <- pivot_wider(dt, id_cols = c("Param","id"),  names_from = c(Time), values_from = c(Recov,CI.LB,CI.UB,`Recovery (95% CI)`) )
dt2$`Recovery [95% CI]` <- ifelse(!is.na(dt2$`Recovery (95% CI)_Meta`), paste0("                 ",dt2$`Recovery (95% CI)_Meta`), paste0("T1 :", paste0(dt2$`Recovery (95% CI)_Time1`), " ; ", "T2: ", paste0(dt2$`Recovery (95% CI)_Time2`)))
dt2$` ` <- paste(rep(" ", 20), collapse = " ")


dt3<-dt2 %>% select(Param,Recov_Meta:11,15,16)

colnames(dt3)[colnames(dt3)=="Param"] <- "   "
colnames(dt3)[colnames(dt3)=="Recovery [95% CI]"] <- "    "

dt3$size_Meta <- ifelse(is.na(dt3$Recov_Meta), NA, 1.2)
dt3$size_T1 <- ifelse(is.na(dt3$Recov_Time1), NA, .6)
dt3$size_T2 <- ifelse(is.na(dt3$Recov_Time2), NA, .6)

# Set-up theme
tm <- forest_theme(
                   ci_pch = c(15, 16, 17),
                   ci_col = c("black","red","blue"),
                   ci_lty = c( "solid", "dashed", "dotted"),
                  # footnote_col = "blue",
                   legend_name = " ",
                  legend_position = "bottom",
                   legend_value = c("Meta-Analysis ", "Time 1 ", "Time 2 "))

library(forestploter)
p <- forest(
  dt3[c(1,12,11)],
            est = list(dt3$Recov_Meta, dt3$Recov_Time1, dt3$Recov_Time2),
            lower = list(dt3$CI.LB_Meta, dt3$CI.LB_Time1, dt3$CI.LB_Time2), 
            upper = list(dt3$CI.UB_Meta, dt3$CI.UB_Time1, dt3$CI.UB_Time2),
            ci_column = 2,
            ref_line = .7,
            xlim = c(-.1, 1),
            ticks_at = c(0, .2, .5, .7, 1),
  sizes = list(dt3$size_Meta, dt3$size_T1, dt3$size_T2),
  theme = tm
)
plot(p)

library(grid)
p <- edit_plot(p,
               row = seq(from=1,to=nrow(dt3),by=7),
               gp = gpar(fontface = c("bold","italic")
                         ), col = 1)

studyInds <- setdiff(1:nrow(dt3),seq(from=1,to=nrow(dt3),by=7))
paramInds <- seq(from=1,to=nrow(dt),by=7)

p <- edit_plot(p, row = studyInds, which = "background",
               gp = gpar(fill = "white"))
p <- edit_plot(p, row = paramInds, which = "background",
               gp = gpar(fill = "grey"))

png("~/Desktop/PD_RecovForest.png", units="in", width=10, height=18, res=300)
plot(p)
dev.off()

null device 
          1 

# ggsave function
ggplot2::ggsave(paste0(saveto,"PD_RecovForest.png"),
                dpi = 300,
                width = 8, height = 17, units = "in", plot=p)

ggplot2::ggsave(paste0(saveto,"PD_RecovForest.tiff"),
                dpi = 600,
                width = 8, height = 17, units = "in", plot=p)